Qwen-CUA: Native Computer Use for (almost) Everything

• URL: http://arxiv.org/abs/2608.02352v1
• Date: Mon, 03 Aug 2026 15:04:20 GMT
• Title: Qwen-CUA: Native Computer Use for (almost) Everything
• Abstract summary: We introduce Qwen-CUA, a native computer-use agent with a mixture-of-experts backbone.<n>It observes only screenshots and acts through keyboard and mouse events, without DOM trees, accessibility metadata, or task-specific APIs.<n>Its scaffold maintains up to 20 active screenshots and folds older visual history in fixed-size blocks to retain recent evidence.
• Score: 104.78368489343713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking, large-scale interactive experience, and learning from sparse yet verifiable outcomes. We introduce Qwen-CUA, a native computer-use agent with a 397B-A17B Qwen mixture-of-experts backbone. It observes only screenshots and acts through keyboard and mouse events, without DOM trees, accessibility metadata, or task-specific APIs. Its scaffold maintains up to 20 active screenshots and folds older visual history in fixed-size blocks to retain recent evidence while preserving reusable prompt prefixes. For training, we build a cloud rollout fleet with access to nearly 100,000 vCPUs and tens of thousands of concurrent environments, construct approximately 40,000 verifiable tasks, and collect personalized long-horizon workflows across everyday and professional software. We optimize complete trajectories with verifiable rewards and trajectory slicing, while iterative training runs refresh supervised data and recalibrate reinforcement-learning tasks. Across eight benchmarks, Qwen-CUA outperforms Qwen3.7 and remains competitive with leading proprietary systems, reaching 86.2 on OSWorld-Verified and 18.5/48.4 binary/partial completion on OSWorld 2.0. Scaling the same recipe to a model with over one trillion parameters yields Qwen-CUA-Max, improving these scores to 87.6 and 21.2/53.3. Qwen-CUA also reduces RedTeamCUA attack success from 36.6 to 16.4 relative to Qwen3.7. Efficiency analyses, a browser deployment, and Bash-augmented experiments further characterize practical behavior. These results establish native computer use as a broadly capable agent foundation and highlight scalable verifiable interaction and hybrid tool use as key directions.

Related papers

• UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations [68.29650478987561]
  UI-Mate is a foundation GUI agent that integrates an environment-grounded training stack with in-context demonstration learning.<n>It sets a new open-weight state of the art on general computer-use benchmarks, scoring 77.0% on OSWorld and 66.2% on WindowsAgentArena.
  arXiv  Detail & Related papers  (2026-08-16T20:59:59Z)
• Dual-Node NVIDIA DGX Spark over Tailscale: A Remote-Access Testbed for Distributed LLM Training and Cyber-Threat-Intelligence Fine-Tuning [0.0]
  This report presents a proof-of-concept deployment of distributed NanoChat pretraining across two NVIDIA DGX Spark systems.<n>The cluster supports a 400-level AI course (CS 426) and a query engine for Security+ POGIL activities in CBS 255.
  arXiv  Detail & Related papers  (2026-08-07T13:41:57Z)
• Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents [27.09949595397669]
  Qwen-UI-Agent is a real-world centric foundation GUI agent spanning mobile, computer-use, web, and DeepSearch environments.<n>Its unified action space interleaves GUI operations with CLI execution and generates batched actions in a single model turn.<n>Online RL supports training on trajectories exceeding 100 turns, with over 10,000 environments accelerating rollout.
  arXiv  Detail & Related papers  (2026-07-30T13:58:41Z)
• OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks [98.07146747892239]
  OSWorld 2.0 is a benchmark of 108 long-horizon computer-use across everyday and professional tasks.<n>Each task represents a realistic end-to-end workflow that takes human users a median of about 1.6 hours to complete.
  arXiv  Detail & Related papers  (2026-06-28T17:59:17Z)
• ENVS: Environment-Native Verified Search for Long-Horizon GUI Agents [35.35193712019351]
  Environment-Native Verified Search builds verified supervision before policy optimization.<n> OSWorld-Noisy is a benchmark for recoverable desktop interruptions.<n> ENVS reaches 30.3 pass@8 on original evaluations and 29.0 on OSWorld-Noisy, outperforming ARPO-style online searches.
  arXiv  Detail & Related papers  (2026-06-22T07:28:55Z)
• ProCUA-SFT Technical Report [80.97543110323542]
  Training computer-use agents (CUAs) requires large-scale, diverse trajectory data collected in full desktop environments.<n>We present ProCUA-SFT, a dataset of 3.1M step-level SFT samples distilled from 93K synthetic trajectories.<n>Fine-tuning UI-TARS 7B on ProCUA-SFT for one epoch yields 45.0% on OSWorld -- an 18.7 percentage-point improvement over the base model.
  arXiv  Detail & Related papers  (2026-06-15T22:04:11Z)
• WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces [27.11445886768225]
  We introduce WeaveBench, a long-horizon hybrid-interface benchmark with 114 tasks across 8 real-world work domains.<n>We evaluate these tasks on a real Ubuntu desktop inside deployed CLI-agent runtimes, augmented with a minimal desktop-control plugin.<n>Across frontier model-runtime pairings, the best PassRate reaches only 41.2%, showing the benchmark remains far from saturated.
  arXiv  Detail & Related papers  (2026-06-08T12:39:23Z)
• CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents [17.596111848044476]
  We introduce CUA-Suite, a large-scale ecosystem of expert video demonstrations and dense annotations for professional desktop computer-use agents.<n>At its core is VideoCUA, which provides approximately 10,000 human-demonstrated tasks across 87 diverse applications.<n> CUA-Suite further provides two complementary resources: UI-Vision, a rigorous benchmark for evaluating grounding and planning capabilities in CUAs, and GroundCUA, a large-scale grounding dataset with 56K annotated screenshots.
  arXiv  Detail & Related papers  (2026-03-25T15:52:56Z)
• GUI-360$^\circ$: A Comprehensive Dataset and Benchmark for Computer-Using Agents [59.107657859025586]
  GUI-360$circ$ is a large-scale, comprehensive dataset and benchmark suite designed to advance computer-using agents (CUAs)<n>The released corpus contains over 1.2M executed action steps across thousands of trajectories in popular Windows office applications.<n>The dataset supports three canonical tasks, GUI grounding, screen parsing, and action prediction, and a hybrid GUI+API action space.
  arXiv  Detail & Related papers  (2025-11-06T12:19:02Z)
• OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents [49.34040731113563]
  We present OSWorld-MCP, the first comprehensive and fair benchmark for assessing computer-use agents' tool invocation, GUI operation, and decision-making abilities.<n> Rigorous manual validation yields 158 high-quality tools, each verified for correct functionality, practical applicability, and versatility.<n> OSWorld-MCP deepens understanding of multimodal agents and sets a new standard for evaluating performance in complex, tool-assisted environments.
  arXiv  Detail & Related papers  (2025-10-28T15:56:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.